1
|
Shimizu R, Wu HT. Unveil sleep spindles with concentration of frequency and time (ConceFT). Physiol Meas 2024; 45:085003. [PMID: 39042095 DOI: 10.1088/1361-6579/ad66aa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 07/19/2024] [Indexed: 07/24/2024]
Abstract
Objective.Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency (TF) analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs).Approach.ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the TF representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and Montreal Archive of Sleep Studies (MASS) benchmark databases. We also quantify spindle IF dynamics.Main results.ConceFT-S achieves F1 scores of 0.765 in Dream and 0.791 in MASS, which surpass A7 and SUMO. We reveal that spindle IF is generally nonlinear.Significance.ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.
Collapse
Affiliation(s)
- Riki Shimizu
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Hau-Tieng Wu
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, United States of America
| |
Collapse
|
2
|
Huang Z, Wing-Kuen Ling B. Joint ensemble empirical mode decomposition and tunable Q factor wavelet transform based sleep stage classifications. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103760] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
3
|
You J, Jiang D, Ma Y, Wang Y. SpindleU-Net: An Adaptive U-Net Framework for Sleep Spindle Detection in Single-Channel EEG. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1614-1623. [PMID: 34398759 DOI: 10.1109/tnsre.2021.3105443] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The sleep spindles in EEG have become one type of biomarker used to assess cognitive abilities and related disorders, and thus their detection is crucial for clinical research. This task, traditionally performed by sleep experts, is time-consuming. Many methods have been proposed to automate this process, yet an increase in performance is still expected. Inspired by the application in image segmentation, we propose a point-wise spindle detection method based on the U-Net framework with an attention module (SpindleU-Net). It maps the sequences of arbitrary-length EEG inputs to those of dense labels of spindle or non-spindle on freely chosen intervals. The attention module that focuses on the salient spindle region allows better performance, and a task-specific loss function is defined to alleviate the problem of imbalanced classification. As a deep learning method, SpindleU-Net outperforms state-of-the-art methods on the widely used benchmark dataset of MASS as well as the DREAMS dataset with a small number of samples. On MASS dataset it achieves average F1 scores of 0.854 and 0.803 according to its consistency with the annotations by two sleep experts respectively. On DREAMS dataset, it shows the average F1 score of 0.739. Its cross-dataset performance is also better compared to other methods, showing the good generalization ability for cross-dataset applications.
Collapse
|
4
|
Ranjan R, Chandra Sahana B, Kumar Bhandari A. Ocular artifact elimination from electroencephalography signals: A systematic review. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.06.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
|
5
|
Zhang J, Wu Y. Competition convolutional neural network for sleep stage classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
6
|
Barstuğan M, Ceylan R. The effect of dictionary learning on weight update of AdaBoost and ECG classification. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2020. [DOI: 10.1016/j.jksuci.2018.11.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
7
|
Yücelbaş Ş. Simple Logistic Hybrid System Based on Greedy Stepwise Algorithm for Feature Analysis to Diagnose Parkinson’s Disease According to Gender. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020. [DOI: 10.1007/s13369-020-04357-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
8
|
Zhang J, Yao R, Ge W, Gao J. Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 183:105089. [PMID: 31586788 DOI: 10.1016/j.cmpb.2019.105089] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 09/19/2019] [Accepted: 09/22/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE In recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learning hierarchical feature representation automatically from raw EEG data have been proposed. However, the state-of-the-art of such methods are quite complex. Using a simple CNN architecture to classify sleep stages is important for portable sleep devices. In addition, employing CNNs to learn rich and diverse representations remains a challenge. Therefore, we propose a novel CNN model for sleep stage classification. METHODS Generally, EEG signals are better described in the frequency domain; thus, we convert EEG data to a time-frequency representation via Hilbert-Huang transform. To learn rich and effective feature representations, we propose an orthogonal convolutional neural network (OCNN). First, we construct an orthogonal initialization of weights. Second, to avoid destroying the orthogonality of the weights in the training process, orthogonality regularizations are proposed to maintain the orthogonality of weights. Simultaneously, a squeeze-and-excitation (SE) block is employed to perform feature recalibration across different channels. RESULTS The proposed method achieved a total classification accuracy of 88.4% and 87.6% on two public datasets, respectively. The classification performances of different convolutional neural networks models were compared to that of the proposed method. The experiment results demonstrated that the proposed method is effective for sleep stage classification. CONCLUSIONS Experiment results indicate that the proposed OCNN can learn rich and diverse feature representations from time-frequency images of EEG data, which is important for deep learning. In addition, the proposed orthogonality regularization is simple and can be easily adapted to other architectures.
Collapse
Affiliation(s)
- Junming Zhang
- College of Information Engineering, Huanghuai University, Henan 463000, China; Henan Key Laboratory of Smart Lighting, Henan 463000, China; Henan Joint International Research Laboratory of Behavior Optimization Control for Smart Robots, Henan 463000, China; Academy of Industry innovation and Development, Huanghuai University, Henan 463000, China
| | - Ruxian Yao
- College of Information Engineering, Huanghuai University, Henan 463000, China; Henan Key Laboratory of Smart Lighting, Henan 463000, China
| | - Wengeng Ge
- College of Information Engineering, Huanghuai University, Henan 463000, China; Henan Key Laboratory of Smart Lighting, Henan 463000, China
| | - Jinfeng Gao
- College of Information Engineering, Huanghuai University, Henan 463000, China; Henan Key Laboratory of Smart Lighting, Henan 463000, China.
| |
Collapse
|
9
|
Md Nor N, Hussain MA, Che Hassan CR. Multi-scale kernel Fisher discriminant analysis with adaptive neuro-fuzzy inference system (ANFIS) in fault detection and diagnosis framework for chemical process systems. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04438-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
10
|
Zhao D, Wang Y, Wang Q, Wang X. Comparative analysis of different characteristics of automatic sleep stages. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 175:53-72. [PMID: 31104715 DOI: 10.1016/j.cmpb.2019.04.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 04/03/2019] [Accepted: 04/05/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE With the acceleration of social rhythm and the increase of pressure, there are various sleep problems among people. Sleep staging is an important basis for the diagnosis of sleep disorders and other related diseases. The process of automatic staging of sleep is mainly divided into three core steps: data preprocessing, feature extraction, and classification. Accurate analysis of the features of sleep electroencephalogram (EEG) signals is not only helpful to improve the accuracy of sleep staging, but also help people to understand their sleep status. METHODS This paper focuses on the analysis of EEG features during sleep staging, and reviews many feature extraction methods and classification methods for sleep staging and sums up these algorithms used in literatures and its staging results. Besides, this paper lists a total of 22 features based on time domain, time-frequency, and nonlinear analysis methods, including kurtosis, skewness, Hjorth parameters, and standard deviations, wavelets energy; sample entropy (SampEn), fuzzy entropy, Tsallis entropy, fractal dimension (FD), complexity. The data set comes from EDF database. Wavelet transform (WT) and support vector machine (SVM) are used to achieve the sleep staging based on single-channel EEG signal. And the characteristic feature data was analyzed by ANOVA. RESULTS By comparison, the SampEn, fuzzy entropy, FD and complexity can achieve ideal sleep staging. The highest accuracy of sleep staging is 85.93%. The FD and complexity are simpler than the entropy value, but their accuracies are lower. Furthermore, the distribution of these methods in different sleep period is more significant than others, which is content with the results of sleep staging. CONCLUSION In a word, due to the non-stationary and non-linear characteristics of EEG signals, time domain and time-frequency analysis methods all have some limitations. Nonlinear analysis was more effective and practical for the analysis of sleep EEG.
Collapse
Affiliation(s)
- Dechun Zhao
- Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Yi Wang
- Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Qiangqiang Wang
- Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Xing Wang
- College of Biomedical Engineering, Chongqing University, Chongqing 400044, China
| |
Collapse
|
11
|
Al-Salman W, Li Y, Wen P. Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features. Front Neuroinform 2019; 13:45. [PMID: 31316365 PMCID: PMC6609999 DOI: 10.3389/fninf.2019.00045] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 05/29/2019] [Indexed: 11/17/2022] Open
Abstract
K-complexes identification is a challenging task in sleep research. The detection of k-complexes in electroencephalogram (EEG) signals based on visual inspection is time consuming, prone to errors, and requires well-trained knowledge. Many existing methods for k-complexes detection rely mainly on analyzing EEG signals in time and frequency domains. In this study, an efficient method is proposed to detect k-complexes from EEG signals based on fractal dimension (FD) of time frequency (T-F) images coupled with undirected graph features. Firstly, an EEG signal is partitioned into smaller segments using a sliding window technique. Each EEG segment is passed through a spectrogram of short time Fourier transform (STFT) to obtain the T-F images. Secondly, the box counting method is applied to each T-F image to discover the FDs in EEG signals. A vector of FD features are extracted from each T-F image and then mapped into an undirected graph. The structural properties of the graphs are used as the representative features of the original EEG signals for the input of a least square support vector machine (LS-SVM) classifier. Key graphic features are extracted from the undirected graphs. The extracted graph features are forwarded to the LS-SVM for classification. To investigate the classification ability of the proposed feature extraction combined with the LS-SVM classifier, the extracted features are also forwarded to a k-means classifier for comparison. The proposed method is compared with several existing k-complexes detection methods in which the same datasets were used. The findings of this study shows that the proposed method yields better classification results than other existing methods in the literature. An average accuracy of 97% for the detection of the k-complexes is obtained using the proposed method. The proposed method could lead to an efficient tool for the scoring of automatic sleep stages which could be useful for doctors and neurologists in the diagnosis and treatment of sleep disorders and for sleep research.
Collapse
Affiliation(s)
- Wessam Al-Salman
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Toowoomba, QLD, Australia.,College of Education for Pure Science, Thi-Qar University, Nasiriyah, Iraq
| | - Yan Li
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Toowoomba, QLD, Australia.,School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, China
| | - Peng Wen
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Toowoomba, QLD, Australia
| |
Collapse
|
12
|
Kremen V, Brinkmann BH, Van Gompel JJ, Stead M, St Louis EK, Worrell GA. Automated unsupervised behavioral state classification using intracranial electrophysiology. J Neural Eng 2018; 16:026004. [PMID: 30277223 DOI: 10.1088/1741-2552/aae5ab] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Automated behavioral state classification in intracranial EEG (iEEG) recordings may be beneficial for iEEG interpretation and quantifying sleep patterns to enable behavioral state dependent neuromodulation therapy in next generation implantable brain stimulation devices. Here, we introduce a fully automated unsupervised framework to differentiate between awake (AW), sleep (N2), and slow wave sleep (N3) using intracranial EEG (iEEG) only and validated with expert scored polysomnography. APPROACH Data from eight patients undergoing evaluation for epilepsy surgery (age [Formula: see text], three female) with intracranial depth electrodes for iEEG monitoring were included. Spectral power features (0.1-235 Hz) spanning several frequency bands from a single electrode were used to classify behavioral states of patients into AW, N2, and N3. MAIN RESULTS Overall, classification accuracy of 94%, with 94% sensitivity and 93% specificity across eight subjects using multiple spectral power features from a single electrode was achieved. Classification performance of N3 sleep was significantly better (95%, sensitivity 95%, specificity 93%) than that of the N2 sleep phase (87%, sensitivity 78%, specificity 96%). SIGNIFICANCE Automated, unsupervised, and robust classification of behavioral states based on iEEG data is possible, and it is feasible to incorporate these algorithms into future implantable devices with limited computational power, memory, and number of electrodes for brain monitoring and stimulation.
Collapse
Affiliation(s)
- Vaclav Kremen
- Department of Neurology, Mayo Systems Electrophysiology Laboratory, Mayo Clinic, 200 First St SW, Rochester, MN 55905, United States of America. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych Partyzanu 1580/3, 160 00 Prague 6, Czechia. Department of Physiology and Biomedical Engineering, Mayo Clinic, 200 First St SW, Rochester, MN 55905, United States of America
| | | | | | | | | | | |
Collapse
|
13
|
Zhang J, Wu Y. Complex-valued unsupervised convolutional neural networks for sleep stage classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 164:181-191. [PMID: 30195426 DOI: 10.1016/j.cmpb.2018.07.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 07/05/2018] [Accepted: 07/25/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Despite numerous deep learning methods being developed for automatic sleep stage classification, almost all the models need labeled data. However, obtaining labeled data is a subjective process. Therefore, the labels will be different between two experts. At the same time, obtaining labeled data also is a time-consuming task. Even an experienced expert requires hours to annotate the sleep stage patterns. More important, as the development of wearable sleep devices, it is very difficult to obtain labeled sleep data. Therefore, unsupervised training algorithm is very important for sleep stage classification. Hence, a new sleep stage classification method named complex-valued unsupervised convolutional neural networks (CUCNN) is proposed in this study. METHODS The CUCNN operates with complex-valued inputs, outputs, and weights, and its training strategy is greedy layer-wise training. It is composed of three phases: phase encoder, unsupervised training and complex-valued classification. Phase encoder is used to translate real-valued inputs into complex numbers. In the unsupervised training phase, the complex-valued K-means is used to learn filters which will be used in the convolution. RESULTS The classification performances of handcrafted features are compared with those of learned features via CUCNN. The total accuracy (TAC) and kappa coefficient of the sleep stage from UCD dataset are 87% and 0.8, respectively. Moreover, the comparison experiments indicate that the TACs of the CUCNN from UCD and MIT-BIH datasets outperform these of unsupervised convolutional neural networks (UCNN) by 12.9% and 13%, respectively. Additionally, the convergence of CUCNN is much faster than that of UCNN in most cases. CONCLUSIONS The proposed method is fully automated and can learn features in an unsupervised fashion. Results show that unsupervised training and automatic feature extraction on sleep data are possible, which are very important for home sleep monitoring.
Collapse
Affiliation(s)
- Junming Zhang
- College of Electronics & Information Engineering, Tongji University, Shanghai 201804, China
| | - Yan Wu
- College of Electronics & Information Engineering, Tongji University, Shanghai 201804, China.
| |
Collapse
|
14
|
A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2578-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|